Bacterial persisters tolerate antibiotic treatment, and underlie the propensity of biofilm infections to relapse. An improved understanding of persister physiology will lead to more effective therapies against biofilm-utilizing pathogens such as Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Knowledge of this rare and transient phenotypic state remains limited due to an inability to isolate persisters from other more abundant cell types. Fluorescence activated cell sorting (FACS) is a leading method to measure persister physiology that circumvents the need for isolation and has the added benefit of quantifying heterogeneity in the persister subpopulation. Unfortunately, FACS is restricted to analyzing only a small number of characteristics in a single experiment. Here we propose to develop a high-throughput (HT) FACS method where system-scale persister physiology and heterogeneity can be quantified in a single experiment. We will accomplish this by using a genome- scale reporter library, next-generation DNA sequencing, and rigorous statistical analyses. We will use FACS to segregate a library of transcriptional reporters into quantiles based on individual cell fluorescence. Each reporter contains a plasmid where gfp is expressed from distinct E. coli promoters. A portion of each quantile will be treated with antibiotics to enumerate persisters, and the remainder will remain untreated. All samples will be plated, and their plasmids harvested. We will use plasmid abundance to approximate reporter colony forming units, and a DNA-barcoded library of the plasmid promoter elements will be generated by PCR for each sample. Deep sequencing of these libraries will provide the abundance of promoter sequences, which will be normalized with the use of internal standards to obtain gene expression distributions. Distances between the distributions (normal cells, persisters) will be statistically compared to identify distinctive patterns. To transform this data into knowledge, we will use experimental (e.g., genetic, single cell) and statistical (e.g., decision trees, clusterin) techniques to elucidate mechanisms underlying differences between persister and normal cell gene expression. To demonstrate the HT method's versatility, we will use it to study three types of persistence that differ in the environment and/or antibiotic used. Completion of this work will provide the first HT assay to measure systems-level persister physiology and heterogeneity, which will be applicable to any characteristic that can be fluorescently-tagged and any organism for which a fluorescent reporter library can be constructed. Further, results from this proposal will provide unprecedented knowledge of persister gene expression and how it differs from normal cells, which will illuminate avenues for therapeutic intervention, and impact the fields of antibiotic tolerance and infectious disease.